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Apple App Store dataset to explore detailed information on app popularity, user feedback, and monetization features. Popular use cases include market trend analysis, app performance evaluation, and consumer behavior insights in the mobile app ecosystem.

Use our Apple App Store dataset to gain comprehensive insights into the mobile app ecosystem, including app popularity, user ratings, monetization features, and user feedback. This dataset covers various aspects of apps, such as descriptions, categories, and download metrics, offering a full picture of app performance and trends.

Tailored for marketers, developers, and industry analysts, this dataset allows you to track market trends, identify emerging apps, and refine promotional strategies. Whether you're optimizing app development, analyzing competitive landscapes, or forecasting market opportunities, the Apple App Store dataset is an essential tool for making data-driven decisions in the ever-evolving mobile app industry.

Dataset Features

• url: The URL linking to the app’s page on the Apple App Store.
• title: The name of the app.
• sub_title: A brief subtitle or tagline for the app.
• developer: The name of the entity or individual that developed the app.
• top_charts: Indicates if the app appears in top charts.
• monetization_features: Information on monetization aspects (such as in-app purchases or advertisements).
• image: A reference to the main app image.
• screenshots: Contains screenshot images of the app.
• description: Detailed app description outlining main features.
• what_new: Details on the latest updates or new features.
• rating: The overall rating based on user reviews.
• number_of_raters: The total number of users who have rated the app.
• reviews_by_stars: Breakdown of the number of reviews by star rating.
• reviews: An aggregation of user reviews.
• events: Any associated events or promotions.
• data_linked_to_you: Indicates if any data is linked to the user.
• seller: The entity responsible for selling or distributing the app.
• category: The category or genre of the app.
• languages: Languages supported by the app.
• copyright: Copyright information provided by the developer.
• size: The file size of the app.
• compatibility: Device or OS compatibility details.
• age_rating: The recommended age rating for the app.
• price: The price of the app.
• In_app_purchases: Details on in-app purchase options.
• support: Information related to app support.
• more_by_this_developer: Suggestions for other apps by the same developer.
• you_might_also_like: Recommendations for similar apps.
• app_support: Additional support details.
• privacy_policy: Link or reference to the app’s privacy policy.
• developer_website: The website of the app developer.
• featured_in: Information on any features or showcases the app has being part of.
• country: The country from which the app’s data was sourced.
• timestamp: A timestamp indicating when the data record was last updated.
• latest_app_version: The most recent version of the app available.
• app_id: A unique identifier for the app.

Distribution

• Data Volume: 36 Columns and 68M Rows
• Format: CSV

Usage

This dataset is versatile and can be used for various applications: - Market Analysis: Analyze app pricing strategies, monetization features, and category distribution to understand market trends and opportunities in the App Store. This can help developers and businesses make informed decisions about their app development and pricing strategies. - User Experience Research: Study the relationship between app ratings, number of reviews, and app features to understand what drives user satisfaction. The detailed review data and ratings can provide insights into user preferences and pain points. - Competitive Intelligence: Track and analyze apps within specific categories, comparing features, pricing, and user engagement metrics to identify successful patterns and market gaps. Particularly useful for developers planning new apps or improving existing ones. - Performance Prediction: Build predictive models using features like app size, category, pricing, and language support to forecast potential app success metrics. This can help in making data-driven decisions during app development. - Localization Strategy: Analyze the languages supported and regional performance to inform decisions about app localization and international market expansion.

Coverage

• Geographic Coverage: Global

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: Can leverage this dataset for training machine learning algorithms and building predictive models concerning app tr

Dataset

This dataset was created by Zakaria Hussain

Contents

Dataset for the paper A Longitudinal Study of Removed Apps in iOS App Store (WWW 2021)

About the Dataset

Context While there are numerous public datasets available, particularly for the Apple App Store (on platforms like Kaggle), there is a noticeable lack of similar datasets for Google Play Store apps. After investigating further, I discovered that the iTunes App Store utilizes a well-organized, index-like structure for easy web scraping. However, Google Play Store relies on more complex modern techniques such as dynamic page loading using JQuery, making it more difficult to scrape the data.

Content Each entry (representing an app) contains attributes like category, rating, size, and other relevant details.

Acknowledgements This dataset was sourced from web scraping the Google Play Store. Without this, the app data would not have been accessible.

Inspiration The data from the Google Play Store offers great potential for driving success in the app development industry. Developers can extract valuable insights to enhance their offerings and effectively tap into the Android market!

A vast collection of data including the 100 New Free Applications in the App Store for each day since February 2024.

Features:

• Date of chart
• Rank
• App name
• App identifier
• Chart collection

Usage:

Market trend analysis, business strategy development.

Coverage:

This will cover the new free app chart data from the UK iOS App store.

License:

CCO

Who can use it:

Product Owners or Project Managers can use this data set.

How to use it:

The data set could be used to track specific applications and their position within the App store chart over time.

A vast collection of data which includes the Top 100 Free Applications in the iOS App Store for each day since February 2024.

Features:

• Date of chart
• Rank
• App name
• App identifier
• Chart collection

Usage:

Market trend analysis, business strategy development.

Coverage:

This will cover the top free app chart in the UK iOS App store.

License:

CCO

Who can use it:

Product Owners or Project Managers can use this data set.

How to use it:

The data set could be used to track specific applications and their position within the App store chart over time.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

Apple’s total revenue amounted to around 391 billion U.S. dollars in their 2024 financial year, a decrease from the historical record of 394.33 billion U.S. dollars in financial year 2022. Apple’s annual revenue quadrupled in the last ten years. The fiscal year end of the company is September, 30th. Apple’s dramatic growth Constant waves of innovative products underly Apple’s drastic growth over the years: the Mac computer, iPhone, iPad, and Apple Watch are all revolutionary products that started their own dynasties and enjoy immense commercial success. Apple’s stock tells an even more impressive story: over the last decade, Apple’s share price has grown more than tenfold and prompted it to become the first trillion-dollar company in terms of market capitalization. As of 2024, Apple is the most valuable brand worldwide. Apple store: a unique invention Huge glass panes, minimalistic design – these are the signature characteristics Apple stores are known for. Opened in the early 2000s, the Apple store contributes yet again to Apple’s success story: it was the fastest retailer worldwide to surpass the one-billion-U.S. dollar annual sales trademark and showcases Apple’s diverse products in hundreds of locations around the globe now. Apple’s home market the United States has the highest concentration of these stores – there are 54 Apple stores in California alone when looking at the number of Apple stores by state .

In this table you will find information about CoronaMelder. This concerns two variabads: 1. The number of people who downloaded CoronaMelder 2. The number of people who warned others via CoronaMelder

1. The number of downloads is based on data from: — App Store (iOS) — Play Store (Android) — Huawei App Gallery (Android)

2. If you have tested positive for corona, you can voluntarily indicate this in the app, together with an employee of the GGD. The figures show how many people have done this.

Past studies have suggested that online reviews positively impact app innovation. However, extant research has not yet explored the distinct impacts of online negative and positive reviews on app innovation. Based on signaling theory and negative bias, this study empirically examines the effects of online negative reviews versus online positive reviews on app innovation by using panel data from the iOS App Store in China. The findings demonstrate that online negative reviews have a more positive influence on app innovation than online positive reviews. Additionally, compared with online positive reviews, app performance more effectively weakens the promoting effect of online negative reviews on app innovation. Moreover, both app history and platform owner’s entry play a positive moderating role in the impact of online negative reviews on app innovation, while no positive moderating effect is observed in the impact of online positive reviews on app innovation. These results demonstrate the different effects of online negative reviews and online positive reviews on app innovation, expand the contingent value of online reviews and app innovation.

The TousAntiCovid app TousAntiCovid is an application that allows everyone to be an actor in the fight against the epidemic. This is an additional barrier gesture that is activated at all times when you have to redouble your vigilance: at the restaurant, in the canteen, when you go to a gym, when you participate in a professional event, when there is a risk that not everyone will respect the other barrier gestures. TousAntiCovid complements the action of doctors and sickness insurance, aimed at containing the spread of the virus by stopping the chains of contamination as soon as possible. The principle is as follows: prevent, while guaranteeing anonymity, people who have been close to a person tested positive, so that they can get tested and taken care of as soon as possible. It also makes it possible to stay informed about the evolution of the epidemic and the conduct to be held and thus to remain vigilant and adopt the right actions. It allows easy access to other tools available to citizens wishing to be involved in the fight against the epidemic: DepistageCovid which gives map of nearby labs and wait times and MesConseilsCovid which provides personalised advice to protect and protect others. The installation of the TousAntiCovid app is done on a voluntary basis. Everyone is supported even if they choose not to use the app. The app is downloaded from the Apple Store and Google Play: Hello.tousanticovid.gouv.fr/ ### Description of the data This dataset informs for each day since the launch of the application on 2 June 2020: — Cumulative total of the number of registered applications minus the number of deregistrations. — Cumulative total of users notified by the application: the number of users notified by the application as risk contacts following exposure to COVID-19, since 2 June 2020. — Cumulative total of users reporting as COVID-19 cases per day: the number of users who reported as COVID-19 cases in the application, since 2 June 2020.

This dataset provides information about Allegheny County vendors accepting WIC who participate in the Pennsylvania Department of Agriculture's Farmers Market Nutrition Program (FMNP). These markets provide the public, including WIC recipients, with fresh, nutritious, locally grown fruits, vegetables, and herbs from approved farmers in Pennsylvania.

Each row in the data includes details about location, days/hours of operation, and the length of the season. Additional directions and affiliations have also been provided when available.

Users may also be interested in the PA Department of Agriculture's new PA FMNP Market Locator app, a free mobile tool to help residents find markets closest to them across the entire state. The FMNP Market Locator app is available both in the Apple Store (https://apple.co/2KNJ4dk) and Google Play (http://bit.ly/2Z86Ytg).

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

SmartParking is a trial designed to help ease traffic congestion and lower travel times by using real-time bay sensor data and the ParkCBR app to show drivers where they are more likely to find available car parking in the Manuka shopping precinct. Android users can download the ParkCBR from GooglePlay Store and iOS users from the AppStore. The Stay dataset shows the utilisation of an area down to a single bay.

This dataset mainly contains data collected from apples infected with different fungi and fresh apples using an electronic nose, The electronic nose contains 8 sensors, and sensor No. 1 and sensor No. 5 use the same sensor to eliminate outliers in the data，If the difference between the 150-300s of the two sensors was greater than 1.2mg/L, the specimen will then be considered anomalous and removed, and the data for removing outlier samples using this method is stored in 'Data/sensors_eliminate' file. The "Fuji" apples selected in this dataset came from apple plantations in Gansu Province, China. 160 ripe apples were selected and randomly divided into 4 groups, 40 apples in each group, namely Group A, Group B, Group C and Group D; The fungi inoculated in the middle apples were Aspergillus niger, Penicillium expansum and Penicillium crustosum. The apple samples were pretreated with 75% alcohol on a sterile bench and dried at room temperature. Then, four holes were punched in four directions of each apple of the three groups A, B, and C with the inoculator. Sample apples were inoculated with 7-day-old molds through drilled loops, and the holes were covered with sterile film. The mold-inoculated apples were then placed in a 1000ml beaker, sealed with plastic wrap, and then placed in a 25°C constant temperature incubator for 5 days. Before the test, the apple samples were taken out of the incubator and placed for 30 minutes. To eliminate the influence of residual gas on the experimental results, electronic nose was cleaned with inert gas before using. Setting electronic nose parameters: cleaning time 500s, collection time 350s, sampling interval 1s, injection flow 150ml/min, the raw data store in 'Data/raw_data' file. Then Matlab is used to preprocess the raw data，The first is to smooth and filter the data, and use 3-point smoothing, 5-point smoothing, 7-point smoothing, 9-point smoothing and 11-point smoothing to smooth and filter the data after removing abnormal samples. The smoothed filtered data is stored in ‘Data/smoothed_data’ file. The second is feature extraction, we take the integral value, variance value, average differential value, maximum gradient value, relatively stable average value and energy value of the response curve of each sensor for 30-300s as the characteristic information of electronic nose. However, the value of 7NE/H2S-1000 and VOC-300 sensors is always 0 during the whole acquisition process, so this dataset only store the data measured by 6 sensors except 7NE/H2S-1000 and VOC-300, which stores in 'Data/feature_parameters_data' file, The second is to use Mahalanobis distance to propose abnormal samples in the data again, and the eliminated data is stored in ‘Data/Data/eliminate_anomalous_data‘ file. Finally, principal component analysis, factor analysis and linear discriminant analysis are used to reduce the dimension of the above data, and the data after dimension reduction is stored in ‘Data/dimensionality_reduction_data' file.

Apple’s iPhone sales accounted for around 55 percent of the company’s overall revenue in the first quarter of fiscal year 2025, the largest share of all Apple products. Over the years, services as well as wearables, home and accessories have made a growing contribution to Apple’s net sales. Apple’s revenue growth amid the pandemic In the first quarter of financial year 2025, Apple’s global revenue reached around 124 billion U.S. dollars. The Americas are Apple’s largest regional market and contributed to around 42 percent of the firm’s sales in that quarter. Who are Apple’s competitors? Having a broad family of products, Apple competes with different companies in different markets. Samsung is Apple’s largest adversaries in the global smartphone market, where the company had a share of almost 21 percent in the second quarter of 2024. Similarly, Apple has a solid position in the PC market without a leading advantage. The situation is reversed in the tablet market and the smartwatch market, where Apple has remained the leader since the early days, staying ahead of Samsung, Huawei, Amazon, etc.

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The Survey Control Information Management System (SCIMS) is a database that contains the coordinates, heights and related attributes for Permanent Survey Marks (PSMs) constituting the State Control Survey. SCIMS online is a tool which enables users to discover and download data related to each survey mark contained within SCIMS. This includes position, accuracy, source and all other technical information, required by surveyors, to fulfil their obligations under NSW legislation when undertaking surveys and creating survey plans.

Content Title	SCIMS Online
Content Type	Web Application
Description	SCIMS online is a toll which enables users to discover and download data related to each survey mark contained within the Survey Control Information Management System (SCIMS).
Initial Publication Date	15/11/2023
Data Currency	15/11/2023
Data Update Frequency	Other
Content Source	Website URL
File Type	Document
Attribution
Data Theme, Classification or Relationship to other Datasets
Accuracy
Spatial Reference System (dataset)	GDA94
Spatial Reference System (web service)	EPSG:4326
WGS84 Equivalent To	GDA94
Spatial Extent
Content Lineage
Data Classification	Unclassified
Data Access Policy	Open
Data Quality
Terms and Conditions	Creative Commons
Standard and Specification
Data Custodian	DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795
Point of Contact	Please contact us via the Spatial Services Customer Hub
Data Aggregator
Data Distributor
Additional Supporting Information
TRIM Number